In breast mass detection, there are many different sizes of masses in the image. However, when the existing target detection model is directly used to detect the breast mass, it is easy to appear the phenomenon of misdetection and missed detection. Therefore, in order to improve the detection accuracy of breast masses, this paper proposed a target detection model D-Mask R-CNN based on Mask R-CNN, which is suitable for breast masses detection. Firstly, this paper improved the internal structure of FPN, and modified the lateral connection mode in the original FPN structure to dense connection. Secondly, modified the size of the anchor of RPN to improve the location accuracy of breast masses. Finally, Soft-NMS was used to replace the NMS in the original model to reduce the possibility that the correct prediction results may be eliminated during the NMS process. This paper used the CBIS-DDSM dataset for all experiments. The results showed that the mAP value of the improved model for detecting breast masses reached 0.66 in the test set, which was 0.05 higher than that of the original Mask R-CNN.Drug resistance and inability to distinguish between cancerous and non-cancerous cells are important obstacles in the treatment of cancer. Zinc oxide nanoparticles (ZnO NPs) is now emerging as a crucial material to challenge this global issue due to its tunable properties. Developing an effective, inexpensive, and eco-friendly method in order to tailor the properties of ZnO NPs with enhanced anticancer efficacy is still challenging. For the first time, we reported a facile, inexpensive, and eco-friendly approach for green synthesis of ZnO-reduced graphene oxide nanocomposites (ZnO-RGO NCs) using garlic clove extract. Garlic has been playing one of the most important dietary and medicinal roles for humans since centuries. We aimed to minimize the use of toxic chemicals and enhance the anticancer potential of ZnO-RGO NCs with minimum side effects to normal cells. Aqueous extract of garlic clove was used as reducing and stabilizing agent for green synthesis of ZnO-RGO NCs from the zinc nitrate and graphene oxide (GO) precursors. A potential mechanism of ZnO-RGO NCs synthesis with garlic clove extract was also proposed. Preparation of pure ZnO NPs and ZnO-RGO NCs was confirmed by powder X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and dynamic light scattering (DLS). The in vitro study showed that ZnO-RGO NCs induce two-fold higher cytotoxicity in human breast cancer (MCF7) and human colorectal cancer (HCT116) cells as compared to pure ZnO NPs. Besides, biocompatibility of ZnO-RGO NCs in non-cancerous human normal breast (MCF10A) and normal colon epithelial (NCM460) cells was higher than those of pure ZnO NPs. This work highlighted a facile and inexpensive green approach for the preparation of ZnO-RGO NCs with enhanced anticancer activity and improved biocompatibility.Prostaglandin E synthases (PGESs) convert cyclooxygenase (COX)-derived prostaglandin H2 (PGH2) into prostaglandin E2 (PGE2) and comprise at least three types of structurally and biologically distinct enzymes. Two of these, namely microsomal prostaglandin E synthase-1 (mPGES-1) and mPGES-2, are membrane-bound enzymes. mPGES-1 is an inflammation-inducible enzyme that converts PGH2 into PGE2. mPGES-2 is a bifunctional enzyme that generally forms a complex with haem in the presence of glutathione. This enzyme can metabolise PGH2 into malondialdehyde and can produce PGE2 after its separation from haem. In this review, we discuss the role of PGESs, particularly mPGES-1 and mPGES-2, in the pathogenesis of liver diseases. A better understanding of the roles of PGESs in liver disease may aid in the development of treatments for patients with liver diseases.Making full use of semantic and structure information in a sentence is critical to support entity relation extraction. Neural networks use stacked neural layers to perform designated feature transformations and can automatically extract high-order abstract feature representations from raw inputs. However, because a sentence usually contains several pairs of named entities, the networks are weak when encoding semantic and structure information of a relation instance. In this paper, we propose a neuralized feature engineering approach for entity relation extraction. This approach enhances the neural network by manually designed features, which have the advantage of using prior knowledge and experience developed in feature-based models. Neuralized feature engineering encodes manually designed features into distributed representations to increase the discriminability of a neural network. Experiments show that this approach considerably improves the performance compared to that of neural networks or feature-based models alone, exceeding state-of-the-art performance by more than 8% and 16.5% in terms of F1-score on the ACE corpus and the Chinese literature text corpus, respectively.Deep attractor networks (DANs) perform speech separation with discriminative embeddings and speaker attractors. Compared with methods based on the permutation invariant training (PIT), DANs define a deep embedding space and deliver a more elaborate representation on each time-frequency (T-F) bin. However, it has been observed that the DANs achieve limited improvement on the signal quality if directly deployed in a reverberant environment. Following the success of time-domain separation networks on the clean mixture speech, we propose a dual-stream DAN with multi-domain learning to efficiently perform both dereverberation and separation tasks under the condition of variable numbers of speakers. The speaker encoding stream (SES) of the dual-stream DAN is trained to model the speaker information in the embedding space defined with the Fourier transform kernels. The speech decoding stream (SDS) accepts speaker attractors from the SES and learns to estimate the early component of the sound in the time domain. Meanwhile, additional clustering losses are used to bridge the gap between the oracle and the estimated attractors. Experiments were conducted on the Spatialized Multi-Speaker Wall Street Journal (SMS-WSJ) dataset. After comparing with the anechoic and reverberant signals, the early component was chosen as the learning targets. https://www.selleckchem.com/products/loxo-292.html The experimental results demonstrated that the dual-stream DAN achieved scale-invariant source-to-distortion ratio (SI-SDR) improvement of 9.8?7.5 dB on the reverberant 2-/3-speaker evaluation set, exceeding the baseline DAN and convolutional time-domain audio separation network (Conv-TasNet) by 2.0?0.7 dB and 1.0?0.5 dB, respectively.